SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–875 of 1963 papers

TitleStatusHype
Structure and Distribution Metric for Quantifying the Quality of Uncertainty: Assessing Gaussian Processes, Deep Neural Nets, and Deep Neural Operators for Regression—0
Evaluating feasibility of batteries for second-life applications using machine learning—0
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning—0
Building 3D Generative Models from Minimal Data—0
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian ProcessesCode0
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control—0
Learning-Based Fault-Tolerant Control for an Hexarotor with Model Uncertainty—0
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference—0
Learning Invariant Weights in Neural Networks—0
Networked Online Learning for Control of Safety-Critical Resource-Constrained Systems based on Gaussian Processes—0
Adaptive Cholesky Gaussian ProcessesCode0
Gaussian Processes and Statistical Decision-making in Non-Euclidean Spaces—0
A Lifting Approach to Learning-Based Self-Triggered Control with Gaussian Processes—0
Nonstationary multi-output Gaussian processes via harmonizable spectral mixtures—0
A Statistical Learning View of Simple KrigingCode0
Fast Inverter Control by Learning the OPF Mapping using Sensitivity-Informed Gaussian Processes—0
The Schrödinger Bridge between Gaussian Measures has a Closed Form—0
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning—0
Multi-model Ensemble Analysis with Neural Network Gaussian Processes—0
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes—0
Variational Nearest Neighbor Gaussian Process—0
Incorporating Sum Constraints into Multitask Gaussian ProcessesCode0
A Kernel-Based Approach for Modelling Gaussian Processes with Functional Information—0
Gaussian Process Position-Dependent Feedforward: With Application to a Wire Bonder—0
Online Time Series Anomaly Detection with State Space Gaussian Processes—0
Show:102550
← PrevPage 35 of 79Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified